R: Complete Data Analysis Solutions

R: Complete Data Analysis Solutions, available at $19.99, has an average rating of 3, with 109 lectures, 12 quizzes, based on 14 reviews, and has 281 subscribers.

You will learn about Extract, transform, and load data from heterogeneous sources Understand how easily R can confront probability and statistics problems Get simple R instructions to quickly organize and manipulate large datasets Predict user purchase behavior by adopting a classification approach Implement data mining techniques to discover items that are frequently purchased together Group similar text documents by using various clustering methods This course is ideal for individuals who are This course is useful whether someone is a hobbyist, analyst, an aspiring or professional data scientist, or even learning data analysis for the first time. Those already familiar with the basics of R, but want to learn to efficiently analyze real-world data problems will also find this course a match for their needs. It is particularly useful for This course is useful whether someone is a hobbyist, analyst, an aspiring or professional data scientist, or even learning data analysis for the first time. Those already familiar with the basics of R, but want to learn to efficiently analyze real-world data problems will also find this course a match for their needs.

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Summary

Title: R: Complete Data Analysis Solutions

Price: $19.99

Average Rating: 3

Number of Lectures: 109

Number of Quizzes: 12

Number of Published Lectures: 109

Number of Published Quizzes: 12

Number of Curriculum Items: 121

Number of Published Curriculum Objects: 121

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Extract, transform, and load data from heterogeneous sources
Understand how easily R can confront probability and statistics problems
Get simple R instructions to quickly organize and manipulate large datasets
Predict user purchase behavior by adopting a classification approach
Implement data mining techniques to discover items that are frequently purchased together
Group similar text documents by using various clustering methods

Who Should Attend

This course is useful whether someone is a hobbyist, analyst, an aspiring or professional data scientist, or even learning data analysis for the first time. Those already familiar with the basics of R, but want to learn to efficiently analyze real-world data problems will also find this course a match for their needs.

Target Audiences

This course is useful whether someone is a hobbyist, analyst, an aspiring or professional data scientist, or even learning data analysis for the first time. Those already familiar with the basics of R, but want to learn to efficiently analyze real-world data problems will also find this course a match for their needs.

If you are looking for that one course that includes everything about data analysis with R, this is it. Let’s get on this data analysis journey together.

This course is a blend of text, videos, code examples, and assessments, which together makes your learning journey all the more exciting and truly rewarding. It includes sections that form a sequential flow of concepts covering a focused learning path presented in a modular manner. This helps you learn a range of topics at your own speed and also move towards your goal of solving data analysis problems with R.

The R language is a powerful open source functional programming language. R is becoming the go-to tool for data scientists and analysts. Its growing popularity is due to its open source nature and extensive development community. R is increasingly being used by experienced data science professionals instead of Python and it will remain the top choice for data scientists in 2017. Big companies continue to use R for their data science needs and this course will make you ready for when these opportunities come your way.

This course has been prepared using extensive research and curation skills. Each section adds to the skills learned and helps us to achieve mastery of data analysis. Every section is modular and can be used as a standalone resource.

This course has been designed to include topics on every possible requirement from a data scientist and it does so in a step-by-step and practical manner. This course covers step-by-step and practical solutions to data analysis using R. It covers every required topic and also adds an introduction to machine learning.

We will start off with learning how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation will be provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We will then understand how easily R can confront probability and statistics problems and look at R instructions to quickly organize and manipulate large datasets. We will then learn to predict user purchase behavior by adopting a classification approach and implement data mining techniques to discover items that are frequently purchased together. Finally, we will offer insight into time series analysis on financial data, after which there will be detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction.

This course has been authored by some of the best in their fields:

Yu-Wei, Chiu (David Chiu)

Yu-Wei, Chiu (David Chiu) is the founder of LargitData, a start-up company that mainly focuses on providing big data and machine learning products. He specializes in using Spark and Hadoop to process big data and apply data mining techniques for data analysis. Yu-Wei is also a professional lecturer and has delivered lectures on big data and machine learning in R and Python, and given tech talks at a variety of conferences.

Selva Prabhakaran

Selva Prabhakaran is a data scientist with a large E-commerce organization. In his 7 years of experience in data science, he has tackled complex real-world data science problems and delivered production-grade solutions for top multinational companies.

Tony Fischetti

Tony Fischetti is a data scientist at College Factual, where he gets to use R everyday to build personalized rankings and recommender systems.

Viswa Viswanathan

Viswa Viswanathan is an associate professor of Computing and Decision Sciences at the Stillman School of Business in Seton Hall University. In addition to teaching at the university, Viswa has conducted training programs for industry professionals. He has written several peer-reviewed research publications in journals such as Operations Research, IEEE Software, Computers and Industrial Engineering, and International Journal of Artificial Intelligence in Education.

Shanthi Viswanathan

Shanthi Viswanathan is an experienced technologist who as a consultant, has helped several large organizations, such as Canon, Cisco, Celgene, Amway, Time Warner Cable, and GE among others, in areas such as data architecture and analytics, master data management, service-oriented architecture, business process management, and modeling.

Romeo Kienzler

Romeo Kienzler is the Chief Data Scientist of the IBM Watson IoT Division and working as an Advisory Architect helping client worldwide to solve their data analysis problems. His current research focus is on cloud-scale data mining using open source technologies including R, ApacheSpark, SystemML, ApacheFlink, and DeepLearning4J.

This course is a blend of text, videos, and assessments, all packaged together keeping your journey in mind. It combines some of the best that Packt has to offer in one complete package. It includes content from the following Packt products:

R for Data Science Cookbook by Yu-Wei, Chiu (David Chiu)
R for Data Science Solutions by Yu-Wei, Chiu (David Chiu)
Mastering R Programming by Selva Prabhakaran
Data Analysis with R by Tony Fischetti
R Data Analysis Cookbook by Viswa Viswanathan and Shanthi Viswanathan
Learning Data Mining with R by Romeo Kienzler

Course Curriculum

Chapter 1: Data Extracting, Transforming, and Loading

Lecture 1: About the course

Lecture 2: Downloading open data

Lecture 3: Reading and writing CSV files

Lecture 4: Scanning text files

Lecture 5: Working with Excel files

Lecture 6: Reading data from databases

Lecture 7: Scraping web data

Lecture 8: Accessing Facebook data

Lecture 9: Working with Twitter

Chapter 2: Data Preprocessing and Preparation

Lecture 1: Renaming the data variable

Lecture 2: Converting data types

Lecture 3: Working with the date format

Lecture 4: Adding new records

Lecture 5: Filtering data

Lecture 6: Dropping data

Lecture 7: Merging and sorting data

Lecture 8: Reshaping data

Lecture 9: Detecting missing data

Lecture 10: Imputing missing data

Chapter 3: Data Manipulation

Lecture 1: Enhancing a data.frame with a data.table

Lecture 2: Managing data with a data.table

Lecture 3: Performing fast aggregation with a data.table

Lecture 4: Merging large datasets with a data.table

Lecture 5: Subsetting and slicing data with dplyr

Lecture 6: Sampling data with dplyr

Lecture 7: Selecting columns with dplyr

Lecture 8: Chaining operations in dplyr

Lecture 9: Arranging rows with dplyr

Lecture 10: Eliminating duplicated rows with dplyr

Lecture 11: Adding new columns with dplyr

Lecture 12: Summarizing data with dplyr

Lecture 13: Merging data with dplyr

Chapter 4: Simulation from Probability Distributions

Lecture 1: Generating random samples

Lecture 2: Understanding uniform distributions

Lecture 3: Generating binomial random variates

Lecture 4: Generating Poisson random variates

Lecture 5: Sampling from a normal distribution

Lecture 6: Sampling from a chi-squared distribution

Lecture 7: Understanding Student's t-distribution

Lecture 8: Sampling from a dataset

Lecture 9: Simulating the stochastic process

Chapter 5: Statistical Inference in R

Lecture 1: Getting confidence intervals

Lecture 2: Performing Z-tests

Lecture 3: Performing student's T-tests

Lecture 4: Conducting exact binomial tests

Lecture 5: Performing Kolmogorov-Smirnov tests

Lecture 6: Working with the Pearson's chi-squared tests

Lecture 7: Understanding the Wilcoxon Rank Sum and Signed Rank tests

Lecture 8: Conducting one-way ANOVA

Lecture 9: Performing two-way ANOVA

Chapter 6: Rule and Pattern Mining with R

Lecture 1: Transforming data into transactions

Lecture 2: Displaying transactions and associations

Lecture 3: Mining associations with the Apriori rule

Lecture 4: Pruning redundant rules

Lecture 5: Visualizing association rules

Lecture 6: Mining frequent itemsets with Eclat

Lecture 7: Creating transactions with temporal information

Lecture 8: Mining frequent sequential patterns with cSPADE

Chapter 7: Time Series Mining with R

Lecture 1: Creating time series data

Lecture 2: Plotting a time series object

Lecture 3: Decomposing a time series

Lecture 4: Smoothing a time series

Lecture 5: Forecasting a time series

Lecture 6: Selecting an ARIMA model

Lecture 7: Creating an ARIMA model

Lecture 8: Forecasting with an ARIMA model

Lecture 9: Predicting stock prices with an ARIMA model

Chapter 8: Text Analytics In-depth

Lecture 1: Scraping web pages and processing texts

Lecture 2: Corpus, TDM, TF-IDF, and word cloud

Lecture 3: Cosine similarity and Latent Semantic Analysis

Lecture 4: Extracting topics with Latent Dirichlet Allocation

Lecture 5: Sentiment scoring with tidytext and Syuzhet

Lecture 6: Classifying texts with RTextTools

Chapter 9: Sources of Data

Lecture 1: Relational databases

Lecture 2: Using JSON

Lecture 3: XML

Lecture 4: Other data formats

Lecture 5: Online repositories

Chapter 10: Let's Do A Project: Social Network Analysis

Lecture 1: Downloading social network data using public APIs

Lecture 2: Creating adjacency matrices and edge lists

Lecture 3: Plotting social network data

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 2 votes
2 stars: 6 votes
3 stars: 0 votes
4 stars: 3 votes
5 stars: 3 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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